A pioneering look at the fundamental role of logic in optimization and constraint satisfaction
While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible modeling and solution techniques. Designed to be easily accessible to industry professionals and academics in both operations research and artificial intelligence, the book provides a wealth of examples as well as elegant techniques and modeling frameworks ready for implementation. Timely, original, and thought-provoking, Logic-Based Methods for Optimization:
* Demonstrates the advantages of combining the techniques in problem solving
* Offers tutorials in constraint satisfaction/constraint programming and logical inference
* Clearly explains such concepts as relaxation, cutting planes, nonserial dynamic programming, and Bender's decomposition
* Reviews the necessary technologies for software developers seeking to combine the two techniques
* Features extensive references to important computational studies
* And much more
| ISBN: | 9780471385219 |
| Publication date: | 14th June 2000 |
| Author: | John Hooker |
| Publisher: | Wiley-Interscience an imprint of Wiley |
| Format: | Hardback |
| Pagination: | 495 pages |
| Series: | Wiley-Interscience Series in Discrete Mathematics and Optimization |
| Genres: |
Mathematics |
A pioneering look at the fundamental role of logic in optimization and constraint satisfaction
While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible modeling and solution techniques. Designed to be easily accessible to industry professionals and academics in both operations research and artificial intelligence, the book provides a wealth of examples as well as elegant techniques and modeling frameworks ready for implementation. Timely, original, and thought-provoking, Logic-Based Methods for Optimization:
* Demonstrates the advantages of combining the techniques in problem solving
* Offers tutorials in constraint satisfaction/constraint programming and logical inference
* Clearly explains such concepts as relaxation, cutting planes, nonserial dynamic programming, and Bender's decomposition
* Reviews the necessary technologies for software developers seeking to combine the two techniques
* Features extensive references to important computational studies
* And much more
Logic-Based Methods for Optimization features in the following genres: Mathematics
Logic-Based Methods for Optimization is available in Ebook, Hardback
Logic-Based Methods for Optimization was written by John Hooker and published by Wiley-Interscience an imprint of Wiley
Logic-Based Methods for Optimization has 495 pages
Yes it is part of Wiley-Interscience Series in Discrete Mathematics and Optimization series